 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UKN7 from www.uniprot.org...
The NucPred score for your sequence is 0.94 (see score help below)
1 MAKEEDEEKKAKKGKKGKKAPEPEKPKRSLKGTSRLFMGFRDRTPKISKK 50
51 GQFRSASAFFWGLHTGPQKTKRKRKARTVLKSTSKLMTQMRMGKKKRAMK 100
101 GKKPSFMVIRFPGRRGYGRLRPRARSLSKASTAINWLTKKFLLKKAEESG 150
151 SEQATVDAWLQRSSSRMGSRKLPFPSGAEILRPGGRLRRFPRSRSIYASG 200
201 EPLGFLPFEDEAPFHHSGSRKSLYGLEGFQDLGEYYDYHRDGDDYYDRQS 250
251 LHRYEEQEPYLAGLGPYSPAWPPYGDHYYGYPPEDPYDYYHPDYYGGPFD 300
301 PGYTYGYGYDDYEPPYAPPSGYSSPYSYHDGYEGEAHPYGYYLDPYAPYD 350
351 APYPPYDLPYHTPYDVPYFDPYGVHYTVPYAEGVYGGGDEAIYPPEVPYF 400
401 YPEESASAFVYPWVPPPIPSPHNPYAHAMDDIAELEEPEDAGVERQGTSF 450
451 RLPSAAFFEQQGMDKPARSKLSLIRKFRLFPRPQVKLFGKEKLEVPLPPS 500
501 LDIPLPLGDADEEEDEEELPPVSAVPYGHPFWGFLTPRQRNLQRALSAFG 550
551 AHRGLGFGPEFGRPVPRPATSLARFLKKTLSEKKPIARLRGSQKARAGGP 600
601 AVREAAYKRFGYKLAGMDPEKPGTPIVLRRAQPRARSSNDARRPPAPQPA 650
651 PRTLSHWSALLSPPVPPRPPSSGPPPAPPLSPALSGLPRPASPYGSLRRH 700
701 PPPWAAPAHVPPAPQASWWAFVEPPAVSPEVPPDLLAFPGPRPSFRGSRR 750
751 RGAAFGFPGASPRASRRRAWSPLASPQPSLRSSPGLGYCSPLAPPSPQLS 800
801 LRTGPFQPPFLPPARRPRSLQESPAPRRAAGRLGPPGSPLPGSPRPPSPP 850
851 LGLCHSPRRSSLNLPSRLPHTWRRLSEPPTRAVKPQVRLPFHRPPRAGAW 900
901 RAPLEHRESPREPEDSETPWTVPPLAPSWDVDMPPTQRPPSPWPGGAGSR 950
951 RGFSRPPPVPENPFLQLLGPVPSPTLQPEDPAADMTRVFLGRHHEPGPGQ 1000
1001 LTKSAGPTPEKPEEEATLGDPQLPAETKPPTPAPPKDVTPPKDITPPKDV 1050
1051 LPEQKTLRPSLSYPLAACDQTRATWPPWHRWGTLPQAAAPLAPIRAPEPL 1100
1101 PKGGERRQAAPGRFAVVMPRVQKLSSFQRVGPATLKPQVQPIQDPKPRAC 1150
1151 SLRWSCLWLRADAYGPWPRVHTHPQSCHLGPGAACLSLRGSWEEVGPPSW 1200
1201 RNKMHSIRNLPSMRFREQHGEDGVEDMTQLEDLQETTVLSNLKIRFERNL 1250
1251 IYTYIGSILVSVNPYQMFGIYGPEQVQQYNGRALGENPPHLFAVANLAFA 1300
1301 KMLDAKQNQCIIISGESGSGKTEATKLILRYLAAMNQKREVMQQIKILEA 1350
1351 TPLLESFGNAKTVRNDNSSRFGKFVEIFLEGGVISGAITSQYLLEKSRIV 1400
1401 FQAKNERNYHIFYELLAGLPAQLRQAFSLQEAETYYYLNQGGNCEIAGKS 1450
1451 DADDFRRLLAAMEVLGFSSEDQDSIFRILASILHLGNVYFEKYETDAQEV 1500
1501 ASVVSAREIQAVAELLQISPEGLQKAITFKVTETMREKIFTPLTVESAVD 1550
1551 ARDAIAKVLYALLFSWLITRVNALVSPRQDTLSIAILDIYGFEDLSFNSF 1600
1601 EQLCINYANENLQYLFNKIVFQEEQEEYIREQIDWQEITFADNQPCINLI 1650
1651 SLKPYGILRILDDQCCFPQATDHTFLQKCHYHHGANPLYSKPKMPLPEFT 1700
1701 IKHYAGKVTYQVHKFLDKNHDQVRQDVLDLFVRSRTRVVAHLFSSHAPQA 1750
1751 APQRLGKSSSVTRLYKAHTVAAKFQQSLLDLVEKMERCNPLFMRCLKPNH 1800
1801 KKEPGLFEPDVVMAQLRYSGVLETVRIRKEGFPVRLPFQGFIDRYCCLVA 1850
1851 LKHDLPANGDMCVSVLSRLCKVMPNMYRVGVSKLFLKEHLYQLLESMREH 1900
1901 VLNLAALTLQRCLRGFFIKRRFRSLRHKIILLQSRARGYLARQRYQQMRR 1950
1951 SLVKFRSLVHAYVSRRRYLKLRAEWRCQVEGALLWEQEELSKREVVAVGH 2000
2001 LEVPAELAGLLQAVAGLGLAQVPQVAPVRTPRLQAEPRVTLPLDINNYPM 2050
2051 AKFVQCHFKEPAFGMLTVPLRTPLTQLPAEHHAEAVSIFKLILRFMGDPH 2100
2101 LHGARENIFGNYIVQKGLAVPELRDEILAQLANQVWHNHNAHNAERGWLL 2150
2151 LAACLSGFAPSPCFNKYLLKFVSDYGRNGFQAVCQHRLMQAMGRAQQQGS 2200
2201 GAARTLPPTQLEWTATYEKASMALDVGCFNGDQFSCPVHSWSTGEEVAGD 2250
2251 ILRHRGLADGWRGWTVAMKNGVQWAELAGHDYVLDLVSDLELLRDFPRQK 2300
2301 SYFIVGTEGPAASRGGPKVVFGNSWDSDEDMSTRPQPQEHMPKVLDSDGY 2350
2351 SSHNQDGTNGETEAQRGTATHQESDSLGEPAVPHKGLDCYLDSLFDPVLS 2400
2401 YGDADLEKPTAIAYRMKGGGQPGGGSSSGTEDTPRRPPEPKPIPGLDAST 2450
2451 LALQQAFIHKQAVLLAREMTLQATALQQQPLSAALRSLPAEKPPAPEAQP 2500
2501 TSVGTGPPAKPVLLRATPKPLAPAPLAKAPRLPIKPVAAPVLAQDQASPE 2550
2551 TTSPSPELVRYSTLNSEHFPQPTQQIKNIVRQYQQPFRGGRPEALRKDGG 2600
2601 KVFMKRPDPHEEALMILKGQMTHLAAAPGTQVSREAVALVKPVTSAPRPS 2650
2651 MAPTSALPSRSLEPPEELTQTRLHRLINPNFYGYQDAPWKIFLRKEVFYP 2700
2701 KDSYSHPVQLDLLFRQILHDTLSEACLRISEDERLRMKALFAQNQLDTQK 2750
2751 PLVTESVKRAVVSTARDTWEVYFSRIFPATGSVGTGVQLLAVSHVGIKLL 2800
2801 RMVKGGQEAGGQLRVLRAYSFADILFVTMPSQNMLEFNLASEKVILFSAR 2850
2851 AHQVKTLVDDFILELKKDSDYVVAVRNFLPEDPALLAFHKGDIIHLQPLE 2900
2901 PPRVGYSAGCVVRRKVVYLEELRRRGPDFGWRFGTIHGRVGRFPSELVQP 2950
2951 AAAPDFLQLPTEPGRGRAAAVAAAVASAAAAQEVGRRREGPPVRARSADH 3000
3001 GEDALALPPYTMLEFAQKYFRDPQRRPQDGLRLKSKEPRESRTLEDMLCF 3050
3051 TKTPLQESLIELSDSSLSKMATDMFLAVMRFMGDAPLKGQSDLDVLCNLL 3100
3101 KLCGDHEVMRDECYCQVVKQITDNTSSKQDSCQRGWRLLYIVTAYHSCSE 3150
3151 VLHPHLTRFLQDVSRTPGLPFQGIAKACEQNLQKTLRFGGRLELPSSIEL 3200
3201 RAMLAGRSSKRQLFLLPGGLERHLKIKTCTVALDVVEEICAEMALTRPEA 3250
3251 FNEYVIFVVTNRGQHVCPLSRRAYILDVASEMEQVDGGYMLWFRRVLWDQ 3300
3301 PLKFENELYVTMHYNQVLPDYLKGLFSSVPASRPSEQLLQQVSKLASLQH 3350
3351 RAKDHFYLPSVREVQEYIPAQLYRTTAGSTWLNLVSQHRQQTQALSPHQA 3400
3401 RAQFLGLLSALPMFGSSFFFIQSCSNIAVPAPCILAINHNGLNFLSTETH 3450
3451 ELMVKFPLKEIQSTRTQRPTANSSYPYVEIALGDVAAQRTLQLQLEQGLE 3500
3501 LCRVVAVHVENLLSAHEKRLTLPPSEITLL 3530
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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